Inverter PQ Control With Trajectory Tracking Capability for Microgrids Based on Physics-Informed Reinforcement Learning-Reference-Cited by-同舟云学术

Inverter PQ Control With Trajectory Tracking Capability for Microgrids Based on Physics-Informed Reinforcement Learning

Published:2024-01 Issue:1 Volume:15 Page:99-112
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

She Buxin¹^ORCID,Li Fangxing¹^ORCID,Cui Hantao²^ORCID,Shuai Hang¹^ORCID,Oboreh-Snapps Oroghene³,Bo Rui³^ORCID,Praisuwanna Nattapat¹,Wang Jingxin¹^ORCID,Tolbert Leon M.¹^ORCID

Affiliation:

1. Department of EECS, The University of Tennessee, Knoxville, TN, USA

2. School of ECE, Oklahoma State University, Stillwater, OK, USA

3. Department of ECE, Missouri University of Science and Technology, Rolla, MO, USA

Funder

U.S. DOD ESTCP Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

https://ieeexplore.ieee.org/ielam/5165411/10372481/10128154-aam.pdf

Reference44 articles.

1. Fusion of Microgrid Control with Model-free Reinforcement Learning: Review and Vision

2. Transient Stability Analysis and Control Design of Droop-Controlled Voltage Source Converters Considering Current Limitation

3. Buffered-Microgrid Structure for Future Power Networks; a Seamless Microgrid Control

4. Stability Assessment and Optimization Methods for Microgrid With Multiple VSG Units

5. Frequency Regulation in Isolated Microgrids Through Optimal Droop Gain and Voltage Control

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